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i-manager's Journal on Software Engineering (JSE)

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Volume 9 Issue 3 January - March 2015

Research Paper

Feature Relevance Analysis in On-Line Marketing to Improve Productivity

P. Dhana Lakshmi* , Kasarapu Ramani, B. Eswara Reddy*

* Assistant Professor, Sree Vidyanikethan Engineering College, Tirupati, India.

Professor and Head, Sree Vidyanikethan Engineering College, Tirupati, India.

* Professor, Jawaharlal Nehru Technological University, Anantapuramu, India.

Lakshmi, P. D., Ramani, K., and Reddy, E. B. (2015). Feature Relevance Analysis in On-Line Marketing to Improve Productivity. i-manager’s Journal on Software Engineering, 9(3), 1-10. https://doi.org/10.26634/jse.9.3.3467

Abstract

Opinion mining applications play a major role in identifying user perspectives. To extract useful information from huge volume of web resources, discussion forums, review sites and blogs is becoming a challenge. Majority of opinion mining approaches for feature extraction is based on static keywords appearing in single product review documents which may omit even relevant reviews. An automated opinion mining mechanism to produce summary of opinions based on a set of product reviews and multiple product features is needed. In this paper a technique for product feature relevance analysis using text mining concepts is proposed. The experiment results on Amazon mobile and office products review data shows the improvement in accuracy and efficiency of the proposed system over existing techniques.

References

[1]. Zhen Hai, Kuiyu Chang, Jung-Jae Kim, and Christopher C. Yang (2014), “Identifying Features in Opinion Mining via Intrinsic and Extrinsic Domain Relevance” IEEE Transactions on Knowledge and Data Engineering, Vol. 26, No. 3,pp.623-634

[2]. T. Wilson, J. Wiebe, and P. Hoffmann (2005), “Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis,” Proc. Conf. Human Language Technology and Empirical Methods in Natural Language Processing, pp. 347-354.

[3] B. Liu (2012), “Sentiment Analysis and Opinion Mining,” Synthesis Lectures on Human Language Technologies, Vol. 5, No. 1, pp. 1-167.

[4]. J. Yu, Z.-J. Zha, M. Wang, and T.-S. Chua (2011), “Aspect Ranking: Identifying Important Product Aspects from Online Consumer Reviews,” Proc. 49th Ann. Meeting of the Assoc. for Computational Linguistics: Human Language Technologies, pp. 1496-1505.

[5]. B. Pang, L. Lee, and S. Vaithyanathan (2002), “Thumbs up? : Sentiment Classification Using Machine Learning Techniques,” Proc. Conf. Empirical Methods in Natural Language Processing, pp. 79-86.

[6]. Wei Jin, Hung Hay Ho (2009), "A Novel Lexicalized HMM-based Learning Framework for Web Opinion t h Mining", Proceedings of the 26 International Conference on Machine Learning, Montreal, Canada.

[7]. Hu Minqing, Liu Bing (2004), “Mining and Summarizing th Customer Reviews”, 10 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, WA, USA.

[8]. Tsytsarau, Mikalai, Palpanas (2011), Themis “Survey on mining subjective data on the web” Data Mining Knowledge Discovery, Vol.24, No.3, pp.478-514.

[9]. Turney Peter (2003), “Measuring praise and criticism: inference of semantic orientation from association”, ACM Transitional Inf. Syst., Vol.24, No.4, pp 315-346.

[10]. Dave Kushal, Lawrence Steve, Pennock, M. David (2003), “Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews”, www2003, Budapest, Hungary.

[11]. Tang, Huifeng, Tan, Songbo, Cheng, Xueqi. (2009), “A survey on sentiment detection of reviews”. Expert Syst. Appl. Vol.36 No. (7), pp.10760–10773.

[12]. Wei Wei, (2011). “Analyzing text data for opinion mining”. Rafael Munoz, Andres Montoyo, Elisabeth Metais (Eds.), Natural Language Processing and Information Systems, Vol.67 No.16, Springer, Berlin/Heidelberg, pp. 330–335.

[13]. AinurYessenalina, Claire Cardie. (2011), "Compositional Matrix-Space Models for Sentiment Analysis", Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pp.172–182.

[14]. Qi Su, XinyingXu, HongleiGuo, ZhiliGuo, XianWu, Xiaoxun Zhang, Bin Swen and Zhong Su. (2008), "Hidden Sentiment Association in Chinese Web Opinion Mining", WWW 2008 / Alternate Track: WWW in China - Mining the Chinese Web April 21-25, 2008 Beijing, China.

[15]. http://snap.stanford.edu/data/web-Amazonlinks. html)

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Research Paper

Real Time Implementation on Media PresentationDescription for MPEG-DASH

DILLI BABU* , Kasarapu Ramani, O. Obulesu*

* M.Tech Scholar, Department of Information Technology, Sree Vidyanikethan Engineering College Tirupati, Andhra Pradesh, India.

Professor & Head, Department of Information Technology, Sree Vidyanikethan Engineering College Tirupati, Andhra Pradesh, India.

* Professor, Department of Information Technology, Sree Vidyanikethan Engineering College Tirupati, Andhra Pradesh, India.

Babu, S. D., Ramani, K., and Obulesu, O. (2015). Real Time Implementation on Media Presentation Description for MPEG-DASH. i-manager’s Journal on Software Engineering, 9(3), 11-20. https://doi.org/10.26634/jse.9.3.3468

Abstract

In video transmission based applications, different users use variety of devices with varying Internet bandwidth. Adaptive Bitrate Streaming (ABS) is a technique which is used to configure the streaming multimedia over network of networks. Adaptive streaming uses a source video format that is encoded at multiple bit rates. Motion Pictures Expert Group (MPEG) - Dynamic Adaptive Streaming over HTTP (DASH) is the latest online adaptive streaming video space technology. In DASH, Media Presentation Description (MPD) is an XML document that contains metadata which is required by a DASH client to construct appropriate HTTP-URLs to access the segment information and to provide the streaming service to the authorized users. The proposed method enables to share production of multiple files, distribution of file and transparent operation to overcome fragmentation and low quality of experience. In this paper, Media presentation description module in DASH is implemented as per the specification of ISO Standard (ISO/IEC 23009-1) second edition 2014-05-15, and the coding guidelines of DVD-FF Record Engine.

References

[1]. Kristian Evensen, Dominik Kaspar, Carsten Griwodz, Pal Halvorsen, Audun F. Hansen, and Paal Engelstad (2011), "Improving the Performance of Quality-Adaptive Video Streaming over Multiple Heterogeneous Access Networks”, Proceedings of the second annual ACM conference on Multimedia systems, pp.57-68.

[2]. ISO/IEC 23009-1,“Dynamic Adaptive Streaming over HTTP – Part 1: Media Presentation Description and Segment Formats”, Information technology.

[3]. ITU-T Rec. H.222.0 | ISO/IEC 13818-1,”Generic coding of moving pictures and associated audio information: Systems”, Information technology.

[4]. Stefan Lederer, Christopher Müller, and Christian Timmerer (2012), “Dynamic Adaptive Streaming over HTTP Dataset”, In Proceedings of the Second ACM Multimedia Systems Conference (MMSys).

[5]. Adobe Http Dynamic Streaming, Available: http://help.adobe.com/en_US/HTTPStreaming/1.0/Using/i ndex.html

[6]. Garson Zhong, and AyubBokani (2014), “A Geo- Adaptive JavaScript Dash Player”, Proceedings of the 2014 Workshop on Design, Quality and Deployment of Adaptive Video streaming, pp.39-40.

[7]. Dynamic Adaptive Streaming over HTTP – wikientry Available:http://en.wikipedia.org/wiki/Dynamic_Adaptive _Streaming_over_HTTP

[8]. Coding of audio-visual objects — Part 12: ISO base media file format (technically identical to ISO/IEC 15444- 12), Information technology, ISO/IEC 14496-12.

[9]. ISO/IEC 23001-8, MPEG systems technologies -- Part 8: Coding-independent code points, Information technology.

[10]. IETF RFC 3023, XML Media Types, January 2001.

[11]. IETF RFC 5261, An Extensible Markup Language (XML) Patch Operations Framework Utilizing XML Path Language (XPath) Selectors, September 2008.

[12]. IETF RFC 5646,Tags for Identifying Languages, September 2009.

[13]. MPEG-DASHIF Player, Available: http://www.dashif. org/reference/players/javascript

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Research Paper

Hybrid Fault Categorization Approach to Reduce theTesting Cost of Combinatorial Test Cases

MOUNIKA MUDUGOTI* , lalitha.B**

* M.Tech Scholar, Department of CSE, JNTUA College of Engineering, Anantapuramu, A.P, India.

** Assistant Professor, Department of CSE, JNTUA College of Engineering, Anantapuramu, A.P, India.

Mounika, M., and Lalitha, B. (2015). Hybrid Fault Categorization Approach to Reduce the Testing Cost Of Combinatorial Test Cases. i-manager’s Journal on Software Engineering, 9(3), 21-26. https://doi.org/10.26634/jse.9.3.3469

Abstract

Modern Software systems are configuring with vast number of options according to the need of the user. So role of the tester is difficult to test those type of customizable systems. Combinatorial Interaction Testing (CIT) approaches such as covering arrays, configuration and parameter testing, systematically samples the configurations and tests only the selected configurations. In this paper, the authors have proposed to combine conclusive evidence and statistical evidence based on fault characterization to develop a hybrid approach. Based on this hybrid mechanism, the authors can improve accuracy in terms of reliability and can reduce the testing cost of combinatorial test cases. The results suggest that the faults exist in practice, and the hybrid approach provides an efficient way to work around the reduction of faults.

References

[1]. Cemal Yilmaz, Emine Dumlu, MyraB. Cohen, Adam Porter, ( 2 0 1 4 ) . “ Reducing Masking Effectsin Combinatorial Interaction Testing: A Feedback Driven Adaptive Approach”, IEEE Transactions on Software Engineering, Vol. 40, No. 1.

[2]. Jacob Chodoriwsky, (2012). “Error Locating Arrays, Adaptive Software Testing, and Combinatorial Group Testing”.

[3]. Justyna Petke, et.al, (2013) “Efficiency and Early Fault Detection with Lower and Higher Strength Combinatorial Interaction Testing”. Proceedings of 9th Joint Meeting on Foundations of Software Engineering, pp.26-36

[4]. Andrea Calvagna;AngeloGargantini, (2009).“ T-wise Combinatorial Interaction Test Suites Construction Based on Coverage Inheritance”, Software Testing, Verification And Reliability Softw. Test. Verif. Reliab., pp.1–7.

[5]. Renée C. Bryce; Yu Lei, ”Combinatorial testing”. pp.1-13

[6]. Javier Ferrer, et.al, (2012), “Evolutionary Algorithm for Prioritized Pair wise Test Data Generation”. Proceedings of 14th Annual Conference on Genetic and Evolutionary Computation, pp.1213-1220

[7]. Mike Papadakis, Christopher Henard, and Yves Le, (2014), “Sampling Program Inputs with Mutation Analysis: Going Beyond Combinatorial Interaction Testing”. EmineDumlu and CemalYilmaz, “Feedback Driven Adaptive Combinatorial Testing”.

[8]. EmineDumlu and Cemal Yilmaz, (2011), “Feedback Driven Adaptive Combinatorial Testing”. Proceedings of International Symposium on Software Testing and Analysis, pp.243-253.

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Research Paper

Optimizing Service Selection in Combinatorial Auction

Sreekanth Reddy*

M.Tech Scholar, Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Anantapur, India.

Reddy, C. S. (2015). Optimizing Service Selection in Combinatorial Auction. i-manager’s Journal on Software Engineering, 9(3), 27-33. https://doi.org/10.26634/jse.9.3.3470

Abstract

The selection of services with the aim to fulfill the quality constraints became critical and challenging to the success of Service-based Systems (SBSs), especially when the quality constraints are ambiguous. However, currently not a single existing method for quality-aware service composition has sufficiently considered non-linear parameters to determine the best service. This paper proposes an enhanced Combinatorial Auction for Service Selection (CASS) method which is capable of handling non linear services. Further the Winner Determination Problem (WDP) will also become a non-linear problem to solve that non linear WDP. This paper considers a binary search algorithm to define the winner based on multiple constraints. Finally the system is tested for the optimal time period calculation for determining the WDP.

References

[1]. Qiang He, Jun Yan, (2014). “Quality-Aware Service Selection for Service-Based Systems Based on Iterative Multi-Attribute Combinatorial Auction”, IEEE Transactions on Software Engineering, Vol. 40, No. 2, pp.192-215.

[2]. Edward Gibbon. The History of the Decline and Fall of the Roman Empire, Vol.1. Strahan & Cadell, 1776-1789.

[3]. J. Hoffmann, P. Bertoli, and M. Pistore, (2007). “Web Service Composition as Planning, Revisited: In Between Background Theories and Initial State Uncertainty,” Proc. 22nd AAAI Conf. Artificial Intelligence (AAAI '05), pp. 1013-1018.

[4]. M. Pistore, A. Marconi, P. Bertoli, and P. Traverso, (2005). “Automated Composition of Web Services by Planning at the Knowledge Level,” Proc. 19th Int'l Joint Conf. Artificial Intelligence (IJCAI '05), pp.1252-1259.

[5]. P.S. Cramton, Y. Shoham, and R. Steinberg, (2006). Combinatorial Auctions. MIT Press.

[6]. P.A. Bonatti and P. Festa, (2005). “On Optimal Service Selection,” Proc. 14th Int'l Conf. World Wide Web (WWW '05), pp.530-538.

[7]. G. Canfora, M.D. Penta, R. Esposito, F. Perfetto, and M.L. Villani, (2006). “Service Composition (Re) Binding Driven by Application-Specific QoS,” Proc. Fourth Int'l Conf. Service-Oriented Computing (ICSOC '06), pp.141- 152.

[8]. M.C. Jaeger, G. M€ uhl, and S. Golze, (2005). “QoSAware Composition of Web Services: A Look at Selection Algorithms,” Proc. IEEE Int'l Conf. Web Services (ICWS '05), pp.807-808.

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[14]. Y. Li, J. Huai, T. Deng, H. Sun, H. Guo, and Z. Du, (2007). “QoS-Aware Service Composition in Service Overlay Networks,” Proc. IEEE Int'l Conf. Web Services (ICWS '07), pp.703-710.

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Review Paper

A Systematic Survey on Waterfall Vs. Agile Vs. LeanProcess Paradigms

Baseer* , Rama Mohan Reddy A, Shoba Bindu C*

* Ph.D Scholar, Department of CSE, JNTUA, Anantapuramu, Andhra Pradesh, India.

Professor, Department of CSE, SVUCE, SV University, Andhra Pradesh, India.

* Associate Professor, Department of CSE, JNTUA, Anantapuramu, Andhra Pradesh, India.

Baseer, K. K., A. Reddy, A. R. M. R., and Bindu, C. S. (2015). A Systematic Survey on Waterfall Vs. Agile Vs. Lean Process Paradigms. i-manager’s Journal on Software Engineering, 9(3), 34-59. https://doi.org/10.26634/jse.9.3.3471

Abstract

We intend to highlight the key features and future directions in the research community of waterfall, agile and lean process paradigms from 2001 to 2014, exemplifying how research on waterfall, agile and lean has progressively increased in the past fourteen years by inspecting articles and papers from scientific and standard publications. Survey materialized in three fold process. Firstly, the authors have investigated on the amalgamation of waterfall, agile and then proceeded with agile-lean. Secondly, they have performed a structural analysis on different author's prominent contributions in the form of tabulation by categories and graphical representation. Thirdly, they huddle with conceptual similarity in the field and also impart a summary table on all process models. In the context of agile, monitoring bottlenecks such as business values and high speed projects, beyond the capacity performed into an organization (CMMI levels) is not clearly defined. But by incorporating lean and agile principles, high quality products can be produced with low cost and desired speed can be achieved in the delivery of projects with optimal capacity. Further, it delivers conclusions by conferring future research directions in the field of software engineering paradigm, such as reducing errors early in the software development process, combining manufacturing principles with lean or agile or both rather than a solo model for providing high quality results. One such type of principle called Poka-Yoke (PY) is a mistake proofing technique used in product design and improves the software development process by integrating software engineering paradigm. A new approach for implementation of Poka-Yoke method in software performance engineering is proposed.

References

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[20]. Matti Kaisti, Tapio Mujunen, Tuomas Mäkilä, Ville Rantala, Teijo Lehtonen, (2014). “Agile Principles in the Embedded System Development”, Agile Processes in Software Engineering and Extreme Programming, 15th International Conference, proceedings, Lecture Notes in Business Information Processing, Vol.179, pp. 16-31.

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[51]. Sun-Wen Chuang, Tainyi Luor, Hsi-Peng Lu, (2014). “Assessment of Institutions, Scholars, and Contributions on Agile Software Development (2001–2012)”, Journal of Systems and Software, Vol.93.

[52]. Stavros Stavru, (2014). “A Critical Examination of Recent Industrial Surveys on Agile Method Usage”, Journal of Systems and Software, Vol.94.

[53]. Giuseppe Destefanis, Steve Counsell, Giulio Concas, Roberto Tonelli, (2014). “Software Metrics in Agile Software: An Empirical Study”, Agile Processes in Software Engineering and Extreme Programming Lecturer Notes in Business Information Processing, pp. 157-170, 2014.

[54]. Suprika Vasudeva Shrivastava, Urvashi Rathod, (2013). “Risks in Distributed Agile Development: A Review,” International Conference on TMS.

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P. Dhana Lakshmi* , Kasarapu Ramani**, B. Eswara Reddy***

* Assistant Professor, Sree Vidyanikethan Engineering College, Tirupati, India. ** Professor and Head, Sree Vidyanikethan Engineering College, Tirupati, India. *** Professor, Jawaharlal Nehru Technological University, Anantapuramu, India.

Lakshmi, P. D., Ramani, K., and Reddy, E. B. (2015). Feature Relevance Analysis in On-Line Marketing to Improve Productivity. i-manager’s Journal on Software Engineering, 9(3), 1-10. https://doi.org/10.26634/jse.9.3.3467

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Real Time Implementation on Media PresentationDescription for MPEG-DASH

DILLI BABU* , Kasarapu Ramani**, O. Obulesu***

Babu, S. D., Ramani, K., and Obulesu, O. (2015). Real Time Implementation on Media Presentation Description for MPEG-DASH. i-manager’s Journal on Software Engineering, 9(3), 11-20. https://doi.org/10.26634/jse.9.3.3468

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Hybrid Fault Categorization Approach to Reduce theTesting Cost of Combinatorial Test Cases

MOUNIKA MUDUGOTI* , lalitha.B**

* M.Tech Scholar, Department of CSE, JNTUA College of Engineering, Anantapuramu, A.P, India. ** Assistant Professor, Department of CSE, JNTUA College of Engineering, Anantapuramu, A.P, India.

Mounika, M., and Lalitha, B. (2015). Hybrid Fault Categorization Approach to Reduce the Testing Cost Of Combinatorial Test Cases. i-manager’s Journal on Software Engineering, 9(3), 21-26. https://doi.org/10.26634/jse.9.3.3469

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Optimizing Service Selection in Combinatorial Auction

Sreekanth Reddy*

M.Tech Scholar, Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Anantapur, India.

Reddy, C. S. (2015). Optimizing Service Selection in Combinatorial Auction. i-manager’s Journal on Software Engineering, 9(3), 27-33. https://doi.org/10.26634/jse.9.3.3470

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A Systematic Survey on Waterfall Vs. Agile Vs. LeanProcess Paradigms

Baseer* , Rama Mohan Reddy A**, Shoba Bindu C***

* Ph.D Scholar, Department of CSE, JNTUA, Anantapuramu, Andhra Pradesh, India. ** Professor, Department of CSE, SVUCE, SV University, Andhra Pradesh, India. *** Associate Professor, Department of CSE, JNTUA, Anantapuramu, Andhra Pradesh, India.

Baseer, K. K., A. Reddy, A. R. M. R., and Bindu, C. S. (2015). A Systematic Survey on Waterfall Vs. Agile Vs. Lean Process Paradigms. i-manager’s Journal on Software Engineering, 9(3), 34-59. https://doi.org/10.26634/jse.9.3.3471

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P. Dhana Lakshmi* , Kasarapu Ramani, B. Eswara Reddy*

* Assistant Professor, Sree Vidyanikethan Engineering College, Tirupati, India.

Professor and Head, Sree Vidyanikethan Engineering College, Tirupati, India.

* Professor, Jawaharlal Nehru Technological University, Anantapuramu, India.

DILLI BABU* , Kasarapu Ramani, O. Obulesu*

* M.Tech Scholar, Department of CSE, JNTUA College of Engineering, Anantapuramu, A.P, India.

** Assistant Professor, Department of CSE, JNTUA College of Engineering, Anantapuramu, A.P, India.

Baseer* , Rama Mohan Reddy A, Shoba Bindu C*

* Ph.D Scholar, Department of CSE, JNTUA, Anantapuramu, Andhra Pradesh, India.

Professor, Department of CSE, SVUCE, SV University, Andhra Pradesh, India.

* Associate Professor, Department of CSE, JNTUA, Anantapuramu, Andhra Pradesh, India.